Zhao Junkang, Zhao Yajie, Han Jiannan, Zhao Yixuan, Liu Sumiao, Liu Zhida, Zhang Liyun, Zhang Yan
Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Shanxi Province Clinical Research Center for Dermatologic and Immunologic Diseases (Rheumatic Diseases), Shanxi Province Clinical Theranostics Technology Innovation Center for Immunologic and Rheumatic Diseases, Taiyuan, China.
Shanxi Academy of Advanced Research and Innovation (SAARI), Taiyuan, China.
Front Nutr. 2025 Jul 16;12:1582674. doi: 10.3389/fnut.2025.1582674. eCollection 2025.
BACKGROUND/OBJECTIVES: Dietary patterns play an important role in regulating serum uric acid (SUA) levels in the body. Recently, compositional data analysis (CoDA) has been proposed as an alternative technique in identifying dietary patterns. However, the relative advantages of CoDA, particularly in identifying dietary patterns associated with hyperuricemia have not been investigated. We evaluated and compared CoDA, including compositional principal component analysis (CPCA) and principal balances analysis (PBA), with the most commonly used principal component analysis (PCA) in determining dietary patterns associated with hyperuricemia.
The 3 day 24-h dietary recall method was used to estimate dietary data from 3,954 study participants of the China Health and Nutrition Survey (CHNS). Dietary patterns were constructed using PCA, CPCA and PBA. These methods were compared based on the performance to identify plausible patterns associated with hyperuricemia.
PCA, CPCA and PBA all identified three dietary patterns, with a common "traditional southern Chinese" pattern high in rice and animal-based foods and low in wheat products and dairy. Only this pattern was positively associated with risk of hyperuricemia [PCA: OR (95%CI) = 1.29 (1.15-1.46); CPCA: OR (95%CI) = 1.25 (1.10-1.40); PBA: OR (95%CI) = 1.23 (1.09-1.38)].
All three dietary patterns methods in our study identified that a "traditional southern Chinese" dietary pattern was associated with increased risk of hyperuricemia, suggesting a robust and consistent finding.
背景/目的：饮食模式在调节体内血清尿酸（SUA）水平方面发挥着重要作用。最近，成分数据分析（CoDA）已被提议作为识别饮食模式的一种替代技术。然而，CoDA的相对优势，特别是在识别与高尿酸血症相关的饮食模式方面尚未得到研究。我们评估并比较了CoDA，包括成分主成分分析（CPCA）和主平衡分析（PBA），与最常用的主成分分析（PCA）在确定与高尿酸血症相关的饮食模式方面的差异。
采用3天24小时饮食回顾法，对中国健康与营养调查（CHNS）的3954名研究参与者的饮食数据进行估算。使用PCA、CPCA和PBA构建饮食模式。根据识别与高尿酸血症相关的合理模式的性能对这些方法进行比较。
PCA、CPCA和PBA均识别出三种饮食模式，其中一种常见的“传统中国南方”模式，大米和动物性食物含量高，小麦制品和乳制品含量低。只有这种模式与高尿酸血症风险呈正相关[PCA：OR（95%CI）=1.29（1.15-1.46）；CPCA：OR（95%CI）=1.25（1.10-1.40）；PBA：OR（95%CI）=1.23（1.09-1.38）]。
我们研究中的所有三种饮食模式方法均表明，“传统中国南方”饮食模式与高尿酸血症风险增加有关，这表明这是一个可靠且一致的发现。
